Statistical methods for understanding multilevel and longitudinal data with application to health studies in South Wales

许多流行病学研究产生纵向数据，即对同一个人或群体进行重复测量。不幸的是，这些测量可能不是在所有的测量时间都对所有的个体可用，这个问题通常被称为缺失。最常见的缺失类型是辍学，即个人错过了一次测量，然后再也没有出现。如果人们因为越来越不健康而辍学，那么随着时间的推移，幸存下来的个体就会变得越来越没有代表性，因为只有更健康的人才会留下来。尽管这可能会给从数据集得出的结论带来偏差，但许多科学家仍然完全忽略了这个问题，因为用于调整退出的方法往往很复杂且耗时。在这项研究中，我将发展我最近提出的处理辍学的技术，并将其应用于威尔士一项为期25年的心血管疾病研究。此外，类似的方法有可能应用于流行病学中常见的另一种结构，即人们聚集在社区内。虽然这些技术将适用于各种各样的情况，但该奖学金将特别侧重于南威尔士的两项健康研究，并将调查该地区身心健康的动态和地理情况。

Many epidemiological studies produce longitudinal data, where there are repeated measurements on the same individuals or groups. Unfortunately, these measurements may not be available for all individuals at all the measurement times, a problem known generically as missingness. The most common type of missingness is dropout, when individuals miss a measurement and then never reappear.If people drop out because they are becoming increasingly unhealthy, then over time the surviving individuals are become less representative, since only the healthier people remain. Though this can introduce bias into conclusions drawn from the dataset, many scientists still ignore the problem entirely because the methods available for adjusting for dropout tend to be complicated and time-consuming. In this fellowship I will develop my recently proposed technique for dealing with dropout, and apply it to a 25-year study of cardiovascular disease in Wales.Additionally, similar methods have the potential to be applied to another structure common in epidemiology, in which people cluster within neighbourhoods. While these techniques will be applicable in a wide variety of situations, this fellowship will focus in particular on two health studies from South Wales, and will investigate the dynamics and geography of physical and mental health in this region.